Predicting Outcomes Via Marketing Asset Analytics

ABSTRACT

Systems and methods for predicting an outcome for a marketing asset. The systems and methods comprise first determining by a computing device, for a marketing asset received by the computing device, at least one asset type of the marketing asset. The marketing asset is parsed, by the computing device, into a plurality of segmented components, based on the determined at least one asset type. For at least one of the plurality of segmented components, at least one discrete marketing message conveyed by the marketing asset is determined, by applying at least one of a predictive model or rules to the at least one segmented component, where the at least one predictive model or rules are stored in a memory associated with the computing device. The method further includes determining, for each of the at least one discrete marketing message, at least one associated score. The method yet further includes inputting the determined at least one associated score to a trained predictive model to obtain a predicted marketing outcome, where the trained predictive model was trained using training scores associated with a multitude of training discrete marketing messages as independent variables and corresponding training marketing outcome data as dependent variables.

RELATED APPLICATIONS

The present application claims priority to U.S. Provisional PatentApplication Nos. 62/970,036 and 62/970,051, both filed on Feb. 4, 2020,the entirety of each of which is incorporated by reference herein.

BACKGROUND

Predicting an outcome of a marketing or sales campaign has typicallyinvolved at least some measure of human guesswork. Marketing and salesdepartments often rely on trial-and-error in their campaigns, in hopesof generating favorable outcomes for at least some fraction of theimplemented campaigns. Such a haphazard approach is highly subjectiveand inefficient, frequently resulting in inefficiencies and highercosts. Moreover, implementing a campaign without a robust prediction oflikely outcomes can result in the campaign being less effective than itmight otherwise be. In some cases, a campaign might even do more harmthan good, such as if certain demographics are offended by a campaign'scontent, for example.

It would be desirable, therefore, to make a data-driven prediction of amarketing or sales campaign's likely outcomes before implementation.Such a prediction could assist in improving efficiency and objectivityfor the creative process underlying the design of the campaign.

SUMMARY

In a first example embodiment, a method for predicting an outcome for amarketing asset may include a computing device performing tasks. Thesetasks may include determining at least one asset type of a marketingasset received by the computing device. The marketing asset is parsed,by the computing device, into a plurality of segmented components, basedon the determined at least one asset type. For at least one of theplurality of segmented components, the method includes determining atleast one discrete marketing message conveyed by the marketing asset, byapplying at least one of a predictive model or rules to the at least onesegmented component, where the at least one predictive model or rulesare stored in a memory associated with the computing device. The methodfurther includes determining, for each of the at least one discretemarketing messages, at least one associated score. The at least oneassociated score is provided as input to a trained predictive model toobtain a predicted marketing outcome for the marketing asset.

In a second example embodiment, a system for predicting an outcome for amarketing asset may include at least one computing device configured toperform tasks. The tasks may include determining at least one asset typeof a marketing asset received by the computing device. The marketingasset is parsed, by the computing device, into a plurality of segmentedcomponents, based on the determined at least one asset type. For atleast one of the plurality of segmented components, the methoddetermines at least one discrete marketing message conveyed by themarketing asset, by applying at least one of a predictive model or rulesto the at least one segmented component, where the at least onepredictive model or rules are stored in a memory associated with thecomputing device. The tasks further include determining, for each of theat least one discrete marketing messages, at least one associated score.Then, the at least one score is input to a trained predictive model toobtain a predicted marketing outcome.

In a third aspect, an article of manufacture for use in predicting anoutcome for a marketing asset is provided. The article of manufactureincludes a non-transitory computer-readable medium having stored programinstructions that, upon execution by one or more processors, cause theone or more processors to perform tasks. The tasks may includedetermining at least one asset type of a marketing asset received by thecomputing device. The marketing asset is parsed, by the computingdevice, into a plurality of segmented components, based on thedetermined at least one asset type. For at least one of the plurality ofsegmented components, the method determines at least one discretemarketing message conveyed by the marketing asset, by applying at leastone of a predictive model or rules to the at least one segmentedcomponent, where the at least one predictive model or rules are storedin a memory associated with the computing device. The tasks furtherinclude determining, for each of the at least one discrete marketingmessage, at least one associated score. Then, the at least one score isprovided as an input to a trained predictive model to obtain a predictedmarketing outcome.

Other aspects, embodiments, and implementations will become apparent tothose of ordinary skill in the art by reading the following detaileddescription, with reference where appropriate to the accompanyingdrawings.

BRIEF DESCRIPTION OF THE FIGURES

The above, as well as additional, features will be better understoodthrough the following illustrative and non-limiting detailed descriptionof example embodiments, with reference to the appended drawings.

FIG. 1 is a schematic block diagram illustrating a marketing outcomeprediction network, according to an example embodiment.

FIG. 2 is a schematic block diagram illustrating a computing device,according to an example embodiment.

FIG. 3 is a schematic block diagram illustrating a supervised learningpipeline, according to an example embodiment.

FIG. 4 is a schematic block diagram illustrating a machine learningpipeline, according to an example embodiment.

FIG. 5 is a schematic block/flow diagram illustrating a system andmethod of predicting outcomes via marketing asset analytics, accordingto an example embodiment.

FIG. 6A is a pictorial screenshot diagram illustrating a graphicaldisplay of brand attributes, according to an example embodiment.

FIG. 6B is a pictorial screenshot diagram illustrating a tabular displayof keyword density, according to an example embodiment.

FIG. 6C is a pictorial screenshot diagram illustrating a tabular displayof image objects and contents, according to an example embodiment.

FIG. 6D is a pictorial screenshot diagram illustrating a graphicaldisplay of product features, according to an example embodiment.

FIG. 6E is a pictorial screenshot diagram illustrating a graphicaldisplay of product experiences, according to an example embodiment.

FIG. 6F is a pictorial screenshot diagram illustrating a graphicaldisplay of differentiations, according to an example embodiment.

FIG. 6G is a pictorial screenshot diagram illustrating a graphicaldisplay of value propositions, according to an example embodiment.

FIG. 6H is a pictorial screenshot diagram illustrating a graphicaldisplay of archetypes, according to an example embodiment.

FIG. 6I is a pictorial screenshot diagram illustrating a graphicaldisplay of reputations, according to an example embodiment.

FIG. 6J is a pictorial screenshot diagram illustrating a graphicaldisplay of habitats, according to an example embodiment.

FIG. 6K is a pictorial screenshot diagram illustrating a graphicaldisplay of pastimes, according to an example embodiment.

FIG. 6L is a pictorial screenshot diagram illustrating a graphicaldisplay of personalities, according to an example embodiment.

FIG. 6M is a pictorial screenshot diagram illustrating a competitivegraphical display of professions, according to an example embodiment.

FIG. 6N is a pictorial screenshot diagram illustrating a competitivegraphical display of brand attributes, according to an exampleembodiment.

FIG. 6O is a pictorial screenshot diagram illustrating a competitivegraphical display of differentiations, according to an exampleembodiment.

FIG. 6P is a pictorial screenshot diagram illustrating a competitivegraphical display of product features, according to an exampleembodiment.

FIG. 6Q is a pictorial screenshot diagram illustrating a competitivegraphical display of personalities, according to an example embodiment.

FIG. 6R is a pictorial screenshot diagram illustrating a competitivegraphical display of pastimes, according to an example embodiment.

FIG. 7 is a pictorial screenshot diagram illustrating output of amarketing outcome prediction, according to an example embodiment.

FIG. 8 is a pictorial screenshot diagram illustrating an example userinterface in the form of a dashboard report, according to an exampleembodiment.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying figures, which form a part of this description. In thefigures, similar symbols typically identify similar components, unlesscontext dictates otherwise. The illustrative embodiments described inthe detailed description, figures, and claims are not meant to belimiting. Other embodiments may be utilized, and other changes may bemade, without departing from the scope of the presented subject matter.It will be readily understood that the aspects of the presentdisclosure, as generally described and illustrated in the figures, canbe arranged, substituted, combined, separated, and designed in a widevariety of different configurations.

Overview

This description sets forth example techniques that relate to systemsand methods for predicting outcomes of marketing assets (also referredto as “communications” herein) using analytics. Such analytics mayinclude, but are not limited to, artificial-intelligence-basedanalytics. These techniques are designed to improve on the typicalpractice of using human guesswork or trial-and-error to assist indesigning marketing or sales campaigns, resulting in potentially higherefficiency and effectiveness in obtaining desired marketing outcomes.The techniques set forth herein are data-driven and include parsing andcategorizing, such as by using artificial intelligence, communicationdata items/components (e.g., text blocks, images, audio, and/or video)into higher-level marketing-based constructs. The marketing-basedconstructs, called “discrete marketing messages” herein, are thenpresented to one or more outcome prediction data models implemented aspart of a machine learning system. Such a machine learning system can behosted on a computing device or system configured to implement a varietyof machine learning models in addition to the aforementionedoutcome-prediction data model(s).

In some examples, the machine learning model may, but need not, includeone or more neural networks or other models (e.g., support vectormachines (SVMs), Bayesian networks, genetic algorithms, linearclassifiers, non-linear classifiers, algorithms based on kernel methods,logistic regression algorithms, linear discriminant analysis algorithms,and/or principal components analysis algorithm) described in furtherdetail below, to make predictions about marketing outcomes. Each modelcan be trained on a training dataset containing source marketing-basedconstructs (i.e., discrete marketing messages) having known marketingoutcomes. These source marketing-based constructs each can be labeledaccording to a number of different types and/or levels of marketingoutcomes. Newly received data items (e.g., text blocks, images, audio,and/or video) received in a marketing communication can be parsed intoindividual data items and placed or categorized into the marketing-basedconstructs, from which marketing outcome predictions can be made usingone or more trained machine learning models.

Various example systems and methods disclosed herein advantageouslyutilize data analysis and manipulation (e.g., parsing) and/or artificialintelligence to analyze marketing content in order to improve efficiencyand reduce subjectivity. Some example embodiments include analyzingdiscrete marketing messages, as well as contents, demographics,emotions, and/or colors of every image and video snippet, for example.This analysis can, in turn, improve marketing-outcome predictionaccuracy. In addition, the methods and systems may link visualcharacteristics to marketing results, to allow for subsequentalterations to improve marketing performance (i.e., better realizationof desired marketing outcomes).

In addition to predicting marketing outcomes through communicationanalytics, various example embodiments of the methods and systems mayadditionally include providing insights and/or recommendations forimproving likelihoods of achieving desired marketing outcomes. Forexample, for the visual characteristics linked to marketing resultsdescribed immediately above, the methods or systems could suggestalterations to visual characteristics, such as introducing additional oralternative colors, or modifying an image or video to display one ormore persons matching a desired target persona, for example.

In some example implementations, the methods and systems may provideanalysis and specific recommendations, so that future marketingcommunications can be drafted based on the recommendations. Additionallyor alternatively, the methods and systems may further provide examplescorresponding to the recommendations.

In some example implementations, the methods and systems couple text andvisual analytics data with performance data to identify patterns thatpredict outcomes. The output may include predictions along withrecommendations on how to improve performance.

Some example embodiments may also assist in improving consistency duringthe copy-drafting process. This is accomplished by the system analyzingthe messaging and keyword density, for example, which helps to ensurethat generated copy is predicted to be effective in producing desiredmarketing results. In some implementations, the system analyzes keywordsthat connect and guide customers to a particular product. By measuringwhich brand attributes both a company and/or its competitors areemphasizing and which ones a company is neglecting, the methods andsystems can assist in promoting consistent usage of effective keywords.

To assist in identifying and maintaining brand standards, in someexample implementations, the methods and systems monitor (e.g., through“following” and recording social media posts) marketing communicationsand document target markets, messaging, and visual identity. This allowsthe system to help ensure that new marketing assets adhere to theidentified brand standards. For example, a particular system could storethe following brand standard: “CompanyA has the best customer service.We are customer-obsessed. It's in our DNA to do whatever is needed to besuccessful.” In addition, the system could compile, store, and presentto a user, via a user interface, keywords and images associated with theidentified brand standard. Example keywords include the following:“Customer success,” “dedicated,” and “customer experience.” Exampleimages might include images of happy employees and happy customers.Finally, some implementations could also provide a list of words and/orimage contents to avoid.

In some further example implementations, the methods and systems comparemarketing performance key metrics with those of industry peers andcompetitors. For example, a server can provide as output a listing oftext brand scores showing company name, rank, and predicted engagementscore for analyzed marketing content. The methods and systems canprovide this output to assist the user in identifying ways to improvefuture marketing efforts.

In some example implementations, the system can also be used for directanalysis of a competitor's marketing and/or sales communications.According to one example, the system's analytics components analyzeevery text block and image, and determine target markets, positioning,messaging, tone, and voice. The system analyzes the contents, emotions,colors, and demographics within every image and illustration, forexample.

The various example method and system implementations described aboveand as further detailed below utilize computing devices and analytics(e.g., artificial intelligence) to help solve technical problemsrelating to predicting marketing outcomes for proposed or actual salesor marketing communication assets. Some of the technical advantagesafforded by these methods and systems include (1) improved predictionaccuracy, (2) the ability to consider a larger pool of data (i.e.,machine-learning training data) than is realistically feasible for humananalysis of marketing communications, and (3) improved efficiency overpast trial-and-error techniques, resulting in faster processing ofproposed communications. Other technical advantages will become clearfrom the following description of the accompanying drawings.

I. Example Marketing Outcome Prediction System

FIG. 1 is a schematic block diagram illustrating a prediction network100, according to one example implementation. Prediction network 100includes computing devices 120, 122 (shown as laptop computers inFIG. 1) and one or more servers 130, connected via network 140. In someexamples, prediction network 100 can have more, fewer, and/or differenttypes of computing devices and/or servers than indicated in FIG. 1.

In example embodiments, some or all of computing devices 120, 122 andserver(s) 130 can be connected to network 140 via one or more, possiblydifferent, network protocols. Data can be transmitted between computingdevices 120, 122 and server(s) 130 over wired and/or wireless linksbetween computing devices, servers, and network 140. The format of eachrespective data transmission between devices in prediction network 100can include one or more of a variety of different formats including:text formats, image formats, extensible mark-up language (XML), SimpleNetwork Maintenance Protocol (SNMP) formats, database tables, a flatfile format, or another format.

Computing devices 120, 122 can create, obtain, update, display, and/ordelete data (and perhaps related software) for configurations ofprediction network 100. Example data for configurations of predictionnetwork 100 includes, but is not limited to: data for configuringdevices in prediction network 100; data for configuring networkprotocols (e.g., File Transfer Protocol (FTP), HyperText TransferProtocol (HTTP), Java Message Service (JMS), Adobe® Page DescriptionFormat (PDF), Simple Object Access Protocol (SOAP), Short MessageService (SMS), Simple Message Transfer Protocol (SMTP), SNMP, TransferControl Protocol/Internet Protocol (TCP/IP), User Datagram Protocol(UDP), Lightweight Directory Access Protocol (LDAP), Message Queue (MQ),and/or other protocols), access-management related data for clientsand/or servers; (e.g., passwords, signatures, credentials, certificates,subscriptions, licenses, and/or tokens related to accessing part or allof the functionality of network 140 and/or cloud-based services,software and/or solutions), and data for customizing, configuring, andmanaging applications on devices/servers of prediction network 100.

One or more servers 130 associated with one or more entities (e.g.,enterprises) may store, update, delete, retrieve, and/or providefunctionality for parsing data and/or learning patterns, trends, and/orfeatures about data related to prediction network 100. Based on thelearned patterns, trends, and/or features, server(s) 130 can generateoutputs, such as predictions about marketing outcomes. The data storedon server(s) 130 may include data relating to analytics models (e.g.,machine learning models), such as training data, for example. The datastored on server(s) 130 may also include device information and/or otherinformation related to devices associated with the network 100. Thestored data can be retrieved from server(s) 130 in response to areceived query (or queries) requesting information about proposed orimplemented marketing or sales campaigns, including predictions aboutmarketing outcomes for such campaigns. In some embodiments, server(s)130 can provide additional or alternative services as well, such asservices related to tracking and storing brand standards.

FIG. 2 is a schematic block diagram illustrating a computing device 200,according to one example implementation. Computing device 200 mayinclude one or more input devices 202, one or more output devices 204,one or more processors 206, and memory 208. In some embodiments,computing device 200 may be configured to perform one or moreherein-described functions of and/or functions related to, for example,some or all of at least the functionality described in the context of anartificial neural network, a convolutional neural network, otheranalytics model, a computer, a personal computer, a smart phone, a smartwatch, a wearable computer, a server device, a printing device, adisplay device, prediction network 100, pipelines 300, 400, and methodsdescribed herein.

Input devices 202 may include user input devices, network input devices,sensors, and/or other types of input devices. For example, input devices202 may include user input devices such as a touch screen, a keyboard, akeypad, a computer mouse, a trackball, a joystick, a camera, a voicerecognition module, and/or other similar devices. Network input devicesmay include wired network receivers and/or transceivers, such as anEthernet transceiver, a Universal Serial Bus (USB) transceiver, or othersimilar transceiver configurable to communicate via a twisted pair wire,a coaxial cable, a fiber-optic link, or a similar physical connection toa wireline network, such as wired portions of network 140, and/orwireless network receivers and/or transceivers, such as a Bluetooth™transceiver, a Zigbee® transceiver, a Wi-Fi™ transceiver, a WiMAX™transceiver, a wireless wide-area network (WWAN) transceiver, and/orother similar types of wireless transceivers configurable to communicatevia a wireless network, such as wireless portions of network 140. Otherinput devices 202 are possible as well.

Output devices 204 may include user display devices, audible outputdevices, network output devices, and/or other types of output devices.User display devices may include one or more printing components, liquidcrystal displays (LCD), light emitting diodes (LEDs), lasers, displaysusing digital light processing (DLP) technology, cathode ray tubes(CRT), light bulbs, and/or other similar devices. Audible output devicesmay include a speaker, speaker jack, audio output port, audio outputdevice, headphones, earphones, and/or other similar devices. Networkoutput devices may include wired network transmitters and/ortransceivers, such as an Ethernet transceiver, a USB transceiver, orother similar transceiver configurable to communicate via a twisted pairwire, a coaxial cable, a fiber-optic link, or a similar physicalconnection to a wireline network, such as wired portions of network 140,and/or wireless network transmitters and/or transceivers, such as aBluetooth™ transceiver, a Zigbee® transceiver, a Wi-Fi™ transceiver, aWiMAX™ transceiver, a WWAN transceiver and/or other similar types ofwireless transceivers configurable to communicate via a wirelessnetwork, such as wireless portions of network 140. Other types of outputdevices may include, but are not limited to, vibration devices, hapticfeedback devices, and non-visible light emission devices, such asdevices that emit infra-red or ultra-violet light. Other output devices204 are possible as well.

Processor(s) 206 can include one or more general purpose processors,central processing units (CPUs), CPU cores, and/or one or more specialpurpose processors (e.g., graphics processing units (GPUs), digitalsignal processors (DSPs), field programmable gated arrays (FPGAs),application specific integrated circuits (ASICs), additionalgraphics-related circuitry/processors, etc.). Processor(s) 206 may beconfigured to execute computer-readable instructions 210 stored inmemory 208 and/or other instructions as described herein.

Memory 208 may include one or more computer-readable storage mediaconfigured to store data and/or instructions that can be read and/oraccessed by at least one of processor(s) 206. The one or morecomputer-readable storage media may include one or more volatile and/ornon-volatile storage components, such as optical, magnetic, organic, orother memory or disc storage, which can be integrated in whole or inpart with at least one of processor(s) 206. The computer-readablestorage media may include one or more components that store data forshort periods of time like register memories, processor caches, and/orrandom-access memories (RAM). The computer-readable storage media mayinclude non-transitory computer readable media that stores program codeand/or data for longer periods of time, such as secondary or persistentlong-term storage; for example, read-only memory (ROM), optical ormagnetic disks, or compact-disc read-only memory (CD-ROM). In someembodiments, memory 208 may be implemented using a single physicaldevice (e.g., one optical, magnetic, organic, or other memory or diskstorage unit), while in other embodiments, memory 208 may be implementedusing two or more physical devices. In particular, memory 208 may storecomputer-readable instructions 210 that, when executed by processor(s)206, may cause a computing device to perform functions, such as but notlimited to, some or all of at least the herein-described functionalityof devices, networks, methods, diagrams, images, equations, and/orscenarios.

In some embodiments, computer-readable instructions 210 may include atleast analytics software 212. Analytics software 212 can includesoftware and/or firmware for providing analytics functionality, makinguse of one or more supervised learning pipelines, such as one or moremachine learning algorithms (MLA) and/or predictive models (trainedMLA). This may include, for example, some or all of at least thefunctionality described in the context of an artificial neural network,a convolutional neural network, a computer, a personal computer, a smartphone, a smart watch, a wearable computer, a server device, a printingdevice, a display device, prediction network 100, pipelines 300, 400,and methods described herein. In some examples, analytics software 212can be updated or otherwise reprogrammed, such as via connections withnetwork 140, to provide different (e.g., improved or otherwise modified)functionality.

Computer-readable instructions 210 may also include a parser 214operable to parse a received marketing communication/asset into one ormore components. Parsing is described in additional detail with respectto FIG. 5, for example.

II. Example Machine Learning Systems

FIG. 3 is a schematic block diagram illustrating a supervised learningpipeline 300, according to one example implementation. Supervisedlearning pipeline 300 includes training data generator 310, traininginput 320, one or more input feature vectors 322, one or more trainingdata items 330, machine learning algorithm 340, actual input 350, one ormore actual input feature vectors 352, predictive model 360, and one ormore predictive model outputs 370. Part or all of supervised learningpipeline 300 may be implemented by executing software for part or all ofsupervised learning pipeline 300 on one or more processors and/or byusing other circuitry, such as specialized hardware (e.g., an embeddedAI processor) for carrying out part or all of supervised learningpipeline 300. The aforementioned processor(s), other circuitry,specialized hardware, and/or embedded AI processor may be situated in acomputing device 200, such as a computing device 200 implementing someor all of the server(s) 130 and/or computers 120 and 122 shown in FIG.1.

In operation, supervised learning pipeline 300 may involve two phases: atraining phase and a prediction phase. The training phase may involvemachine learning algorithm 340 learning one or more tasks. Theprediction phase may involve predictive model 360, which may be atrained version of machine learning algorithm 340, making predictions toaccomplish the one or more learned tasks. In some examples, machinelearning algorithm 340 and/or predictive model 360 may include, but isnot limited to, one or more artificial neural networks (ANNs), deepneural networks, convolutional neural networks (CNNs), recurrent neuralnetworks, support vector machines (SVMs), Bayesian networks, geneticalgorithms, linear classifiers, non-linear classifiers, algorithms basedon kernel methods, logistic regression algorithms, linear discriminantanalysis algorithms, and/or principal components analysis algorithms.Other algorithms and/or models may additionally or alternatively beused.

During the training phase of supervised learning pipeline 300, trainingdata generator 310 may generate training input 320 and training dataitem(s) 330. Training input 320 can be processed to determine inputfeature vector(s) 322. In some examples, training input 320 may bepreprocessed, such as for image recognition tasks. In other examples,training input 320 may include one or more images. Specifically,training input 320 may include images that exhibit known customerresponses (e.g., positive, negative, or neutral) associated with theimage content. The training sets used in various embodiments set forthherein may include data, such as labeled training data, pertaining tohundreds of thousands of brands and millions of marketing assets, forexample. As a result, even small businesses, or those with limitedresources, may benefit from the technical improvements afforded by thetechnologies of the methods and systems set forth.

Input feature vector(s) 322 may be provided to machine learningalgorithm 340 to learn one or more tasks. Input feature vector(s) 322may represent features associated with training input 320.

Machine learning algorithm 340 may generate one or more outputs based oninput feature vector(s) 322 and perhaps training input 320 related tothe one or more tasks being learned. During training, training dataitem(s) 330 may be used to assess the output(s) of machine learningalgorithm 340 for accuracy, and machine learning algorithm 340 may beupdated based on this assessment. More particularly, the assessment mayinclude use of a loss function that measures inconsistencies betweenpredicted values and ground truth values (e.g., data labels from atraining data item). For example, a loss function LF1 for trainingmachine learning algorithm 340 may be based on a mean-square-error (MSE)value representing a “Euclidean distance” between the predicted value(LF1(O_(x))) and a data label of a training data item (O_(x)). Theoutput of the loss function may be used during the training of machinelearning algorithm 340 to decide how much to adjust parameters to reducean error value (e.g., an error value calculated based on a differencebetween O_(x) and LF1(O_(x)), such as |O_(x)−LF1(O_(x))|(an L1 norm) or|{O_(x)−LF1(O_(x))}²|^(1/2) (an L2 norm)). Other loss functions arepossible as well. For example, the loss function may be based oncross-entropy loss and/or other values.

In a more specific example of training, training input 320 may be animage in a marketing communication, and training data item(s) 330 mayinclude one or more labels that indicate aspects (e.g., typical specificcustomer reaction or feeling for a particular demographic) associatedwith the image. During training, machine learning algorithm 340 may makepredictions about aspects associated with training input 320.Predictions may be compared to the label(s) of training data item(s) 330to see if they match. For example, if machine learning algorithm 340detects the same aspects as mentioned in a label for training dataitem(s) 330, then they match; otherwise, an error can be determined. Avector of errors may be calculated, corresponding to a magnitude ofdifference between the predicted aspects and the labels of training dataitems(s) 330. Then, a loss function may compare the vector of errorspredicted by machine learning algorithm 340 with a corresponding vectorconstructed from the labels of aspects in training data item(s) 330. Thecomparison may be used to adjust parameters of machine learningalgorithm 340 to reduce the overall error value.

Moreover, with respect to artificial neural networks (ANN), an ANN mayhave a number of nodes, each node having an associated numerical weight.These nodes may be arranged in the ANN into one or more layers (e.g.,such as one or more CNN layers for each CNN model(s) 420 as describedbelow). Training an ANN to perform a task may involve changing theweights of the nodes as training inputs related to the task are providedto and processed by the ANN. Once trained, the ANN (or model) may or maynot modify the weights of the nodes upon being deployed forinference/detection/prediction tasks.

Training of machine learning algorithm 340 may continue until machinelearning algorithm 340 is considered to be trained to perform the one ormore tasks. For example, training of machine learning algorithm 340 maycontinue until the occurrence of one or more training terminationcriteria. The training termination criteria may include, but are notlimited to, criteria where machine learning algorithm 340 (a) has madepredictions for IN items of training input 320 (e.g., IN=1, 10, 100,1000, 5000, 50000, 10000000, etc.), (b) has made predictions for eachitem of training input 320 a number IT times (e.g., IT=1, 2, 3, . . . ),or (c) has made predictions for a number N of items of validation inputwith an accuracy that exceeds a threshold accuracy value (e.g., For N=1,10, 25, 50, 100, 1000, etc., a corresponding threshold accuracy of 50%,90%, 95%, 99%, 99.9%, etc.). Other examples are also possible. Oncetrained, machine learning algorithm 340 may be considered to be apredictive model, such as predictive model 360.

During the prediction phase of supervised learning pipeline 300, actualinput 350 can be processed to generate actual input feature vector(s)352. Predictive model 360 can perform predictions, such as imagerecognition tasks, by taking actual input feature vector(s) 352 asinputs.

In example embodiments, predictive model 360 may generate one or morepredictions about actual input feature vector(s) 352 as part or all ofpredictive model output(s) 370. For instance, predictive model 360 mayreceive a request to make a prediction about actual feature inputvector(s) 352, responsively generate one or more predictions aboutactual input feature vector(s) 352, and provide the prediction(s) aspredictive model output(s) 370. The prediction phase/use of predictivemodel 360 is discussed in more detail below.

In some examples, machine learning algorithm 340 may be trained on oneor more training computing devices and predictive model 360 may beexecuted on the same training computing device(s). In other examples,machine learning algorithm 340 may be trained on the training computingdevice(s). Then, after training, now-trained machine learning algorithm340 may be communicated as predictive model 360 from the trainingcomputing device(s) to one or more other computing devices that canexecute predictive model 360 to generate predictive model output(s) 370.

FIG. 4 is a schematic block diagram illustrating a machine learningpipeline 400, according to one example implementation. In some examples,ML pipeline 400 may be related to and/or implement the prediction phaseof supervised learning pipeline 300. ML pipeline 400 includes actualinput 350, actual feature input vector(s) 352, preprocessing 410,predictive model 360, post-processing 440, and predictive modeloutput(s) 370. Part or all of ML pipeline 400 may be implemented byexecuting software on one or more processing devices and/or by usingother circuitry (e.g., specialized hardware for carrying out part or allof ML pipeline 400).

In operation, ML pipeline 400 may begin by receiving actual input 350.In particular, actual input 350 to ML pipeline 400 may include one ormore actual electronic images. After receiving actual input 350, MLpipeline 400 may generate actual input feature vector(s) 352 based onactual input 350. Actual feature vector(s) 352 may represent features ofactual input 350. The feature(s) may include, but are not limited tocolor values, size, and others.

After generating actual input feature vector(s) 352, ML pipeline 400 mayproceed to pre-processing 410. During pre-processing 410, actual inputfeature vector(s) 352 may be pre-processed to increase the accuracy ofpredictive model 360. For example, pre-processing 410 might includenormalization and mean-subtraction for rescaling numeric attributes intoa more compact range principal component analysis (PCA) to reduce thedimensionality. As another example, pre-processing 410 might includewhitening transformations to reduce underlying noise in input data.

After pre-processing 410, ML pipeline 400 may proceed to use predictivemodel 360 to make predictions regarding pre-processed actual inputfeature vector(s) 352. FIG. 4 shows an example in which predictive model360 of ML pipeline 400 includes CNN model(s) 420. When predictive model360 receives pre-processed actual input feature vector(s) 352 frompre-processing 410, predictive model 360 may utilize CNN model(s) 420 toclassify (or otherwise characterize) pre-processed actual input featurevector(s) 352. The input (i.e., pre-processed actual input featurevector(s) 352) into CNN model(s) 420 may be referred to herein aspre-processed input 412.

CNN model(s) 420 may include, for example, one or more trainedconvolutional neural networks that perform classification onpre-processed input 412. In particular, each CNN model in CNN model(s)420 may contain one or more CNN layers. A CNN layer may performconvolution, activation, pooling, or inference tasks using respectiveone or more convolution layers, one or more activation layers, one ormore pooling layers, and/or one or more fully-connected layers.Convolution layers may include one or more filters to filter respectiveinputs. Each filter may work over a subset of an input.

The output of the convolution layer (i.e., activation map) may beprovided as an input to an activation layer. Activation layers determinewhether the activation map is to be provided to a subsequent layer. Forexample, suppose the activation map has values in the range [0, 1] andthat only activation map values above 0.4 are to be provided to asubsequent layer. Then, the activation layer could map activation mapvalues in the range [0, 0.4] to 0 (representing no activation), and mapactivation map values in the range (0.4, 1] to 1 (representingactivation). More generally, an activation layer can model an activationfunction, such as a sigmoid/logistic activation function, a hyperbolictangent activation function, a rectified linear unit (ReLU) activationfunction, or another activation function, to determine whether theoutput of the convolution layer (e.g., the activation map) is to beprovided to a subsequent layer. Pooling layers down-sample inputprovided by convolution and activation layers.

Fully-connected layers can have full connections to all activations in aprevious layer and may act as a soft-max classifier. That is, thefully-connected layer can receive an input from a previous layer and canoutput a resulting N dimensional vector, where N is the number ofpossible predictions that could be made about actual input 350. Forexample, if actual input 350 was an image of a digit and if predictivemodel 360 was trained to classify digits within images, N would be 10.

Each output number in the resulting N dimensional vector may representthe probability of a certain class (i.e., possible prediction) occurringin actual input 350. For example, continuing from the example digitclassifier, a resulting vector could appear as [0 0.1 0.1 0.75 0 0 0 0 00.05], where the probability that actual input 350 contains the digit 1is 10%, the probability that actual input 350 contains the 2 is 10%, andso on. This resulting vector may be outputted by CNN model(s) 420, forexample, as output vector 430. In practice, CNN model(s) 420 can includeCNNs with different combinations (also known as CNN architectures) ofconvolutional layers, activation layers, pooling layers, andfully-connected layers.

As a conceptual illustration of CNN layers and their operations, CNNlayers 422, 424, 426, and 428 are provided in FIG. 4. CNN layers 422,424, 426 can selectively filter and down-sample input using convolution,activation, and pooling layers to extract features, while CNN layer 428can provide a final output prediction about actual input 350. In anexample operation, preprocessed input 412 may be provided to CNN layer422. Then, CNN layer 422 may process preprocessed input 412 (e.g.,convolve, activate, and/or pool preprocessed input 412 into a firstfeature vector) and provide input (e.g., the first feature vector) toCNN layer 424. CNN layer 424 may process the input provided by CNN layer422 (e.g., convolve, activate, and/or pool input provided by CNN layer424 into a second feature vector) and provide input (e.g., the secondfeature vector) to CNN layer 426. CNN layer 426 may process the inputprovided by CNN layer 424 (e.g., convolve, activate, and/or pool inputprovided by CNN layer 424 into a third feature vector) and provide input(e.g., the third feature vector) to CNN layer 428. CNN layer 428 mayprocess the input provided by CNN layer 426 (e.g., make a soft-maxprediction on input provided by CNN layer 426 into a fourth featurevector) and provide output vector 430 (e.g., the fourth feature vector)as the output prediction of predictive model 360. After predictive model360 has made a prediction (i.e., output vector 430) regarding actualinput 350, ML pipeline 400 may proceed to perform post-processing 440 ofoutput vector 430. It should be noted that CNN layers 422, 424, 426, and428 are solely a convenient conceptual representation of a CNNarchitecture and not intended to be limiting with respect to exampleembodiments or techniques described herein. For example, CNN model(s)420 may have more, fewer, and/or different layers than CNN layers 422,424, 426, and 428. Moreover, predictive model 360 may utilize othermodels besides CNN models.

In some examples, post-processing 440 may include weighting methods thatcombine multiple predictions into a single, aggregate prediction. Forinstance, output vector 430 may represent one out of a myriad ofpredictions made by predictive model 360 with regards to actual input350. As a specific example, if actual input 350 is an image, multiplesections of the image (e.g., 4 sections) may each be provided topredictive model 360. As such, output vector 430 may represent one outof a total of four predictions made by predictive model 360 with regardto actual input 350. Then, weighting methods may be used to combine eachof the four predictions into a single, aggregate prediction. Weightingmethods may be determined as a sum, or other numerical combination(e.g., an average, a weighted average, a product) of the individualoutput vectors.

In some examples, post-processing 440 may include ranking methods thatcurtail output vector 430 to only “relevant” classes. Continuing againfrom the digit classifier example, suppose that output vector 430 frompredictive model 360 is [0 0.1 0.1 0.75 0 0 0 0 0 0.05]. In such ascenario, post-processing 440 may rank the top N classes as “relevant”and only retain classes with predicted probabilities that fall into thetop N. For instance, if N=3, ranking methods may retain digit 3 (75%predicted probability), digit 2 (10% predicted probability), and digit 1(10% predicted probability). In another example, post-processing 440 mayretain any class with a prediction value exceeding a predefinedthreshold (e.g., 60% or 80%). Many other examples of ranking methods arealso possible. In line with the discussion above, ranking methods mayalso be applied to single, aggregate vectors determined by weightingmethods.

After post-processing 440 of the output(s) of predictive model 360 iscomplete, ML pipeline 400 may proceed to provide predictive modeloutput(s) 370. ML pipeline 400 may provide predictive model output(s)370 by storing some or all of predictive model output(s) 370,communicating some or all of predictive model output(s) 370 to one ormore other computing devices, displaying some or all of predictive modeloutput(s) 370, and/or otherwise furnishing some or all of predictivemodel output(s) 370 as outputs.

In some examples, specific hardware and/or software may be designed toembody part or all of predictive model 360 and/or ML pipeline 400. Forexample, the specific hardware representing predictive model 360 and/orML pipeline 400 may be embodied as analytics software 212 executed byprocessor(s) 206.

III. Example Systems and Methods

FIG. 5 is a schematic block/flow diagram illustrating a method 500 ofpredicting marketing outcomes for a marketing and/or salescommunication, according to an example implementation. Method 500 mayrepresent a specific sequence or series of actions that, when performed,allows a computing device to predict outcomes for marketingcommunications. Method 500 can be carried out by a computing device,such as computing device 200. Moreover, additional components, steps, orblocks, may be added to method 500 without departing from the scope ofthe method.

The method 500 includes receiving a communication (block 502), such asthrough the input device 202. Examples of communications could beproposed or implemented marketing and/or sales communications (alsoreferred to as “assets” herein), in the form of data or otherinformation representing or embodying such communications. The receivedcommunication may be associated with the user (e.g., an individual orenterprise) of the method 500, or instead may be associated with acompetitor of the user. The communication may be targeted toward anindividual (e.g., a “form” email customized for a particularindividual), a group of individuals (e.g., a customer newsletter or blogpost), or the general public (e.g., a scanned print advertisement or webad).

The method 500 further includes determining a type of asset (block 504)encompassed by the received communication. For example, thecommunication could be determined to be an asset of one of the followingtypes: a web page 508, a social media post 510, a pdf 512, an electronicdocument (e.g., google doc 514, MS-Word doc, etc.), a plain-text email516, an html email 518, a video 520, a sales call (e.g., a call scriptor transcript of a recorded call), a digitization of a traditional printcommunication, or some other marketing or sales asset. Determining thetype of asset could include examining at least a portion (e.g., aninitial portion, such as a header or remarks section) of a data fileassociated with the particular received communication. Alternatively,determining the type of asset could include examining the entirecommunication.

In some example implementations, blocks 502 and 504 may be swapped, suchthat a type of marketing asset/communication is first determined ordecided, followed by actually receiving marketing assets/communicationsof that type. For example, web communications may be received from acollection service that “crawls” websites, scraping data for use in theoutcome prediction methods set forth herein. In such a case, it is firstdecided that web communications are going to be examined, and then, thecommunications (e.g., web pages) are received via a robotic web crawler.

Moreover, the method 500 could additionally or alternatively includeclassifying the communication by one or more attributes (block 505),such as marketing objective or intended outcome (e.g., to initiate asale, to notify and/or inform, or to build a relationship). For example,the following are specific examples of classification attributes thatcould be assigned to a particular marketing or sales communication:business vertical (e.g., home services, electronic retail, soft drinks),brand archetype (e.g., explorer, lover, jester), customer type (e.g.,business, consumer), supply chain role (e.g., maker, distributor,retailer), etc. Such a classification could be assigned by a creator ofthe communication, for example. For instance, during a sign-up orregistration process, the communication creator could supply informationvia a web form to create a digital “brand book” that indicates potentialclassification attributes, such as industry or other attributes listedimmediately above. Alternatively, the system 500 could apply aclassification model to the received communication to determine asuitable classification attribute. As yet another alternative, a userassociated with the system 500 (where the user is unaffiliated with thecommunication creator) could apply a classification. While block 505 isillustrated as an optional function to be performed after the functionafter block 504, the function of block 505 could alternatively be movedto other locations in the method (e.g., immediately after block 502 orelsewhere) or omitted entirely. The function of classifying thecommunication by one or more attributes shown in block 505 may assistwith predicting the marketing outcome, such as by informing as tosuitable or preferred machine learning models (or sequences of models)and/or by defining a particular industry, vertical, or other group towhich the received communication should be evaluated and/or comparedagainst.

The method 500 yet further includes parsing the communication (block506) into one or more blocks (e.g., blocks 508-520, as described above),based on the determined type of asset. For example, based on thedetermined type of asset, the communication could be parsed into one ormore of the following: image(s) 522, video(s) 524, audio 526, and/ortext 528. This parsing could include utilizing one or more of thefollowing to parse the communication into blocks: html tags, ApplicationProgram Interfaces (APIs), image recognition, and/or speechtranscription, for example.

More particularly, and by way of example only, if the asset(communication) is a web page, then the method 500 could includerobotically crawling the web page, identifying and storing html code,and parsing the stored html code by identifying html tags. For example,each html tag could identify at least one image, headline text,sub-headline text, paragraph text, and/or video.

If the asset is a social media post, then the method 500 could includeusing an API of the respective social media app to identify segmentedcomponents, such as image, text, and/or video components. Alternatively,if the asset is a social media post on a social media website, then themethod could include crawling the web page, identifying and storing htmlcode, and parsing the stored html code by identifying html tags, wherethe html tags identify an image, headline text, sub-headline text,paragraph text, and/or video, for example.

If the asset is a pdf, then the method 500 could include using a pdfparser to identify segmented components, such as image, text, or videocomponents.

If the asset is a google doc (or other similar file created with aweb-based application), then the method 500 could include using an APIassociated with the web-based application (e.g., a google doc app) toidentify segmented components, such as image, text, or video.

If the asset is a plain-text email, then the method 500 could includeparsing the text block of the email.

If the asset is an html email, then the method 500 could include parsingthe html email by identifying html tags associated with at least oneimage, headline text, sub-headline text, paragraph text, and/or video.

If the asset is a video, then the method 500 could include segmentingthe video into temporal blocks, parsing the audio and images from eachblock, and transcribing the audio into text, such as by using an onlineor local automated audio transcription service. One suitable audiotranscription service is Amazon's audio transcription service, availableat https://aws.amazon.com/transcribe/. The parsed video could be furtherparsed into a series of images (e.g., all images in the parsed video ora subset of images in the parsed video), to which image recognitionsoftware (e.g., using predictive classification models similar to thosedescribed herein) could be applied to identify one or more objects,scenes, or other items present in the parsed video.

Similarly, if the asset is an audio sales call, then the method 500could include segmenting the call audio into temporal blocks, parsingthe segmented audio from each block, and transcribing the parsed audiointo text.

If the asset is an asset other than one listed above, then the method500 could identify segmented components using an API for an applicationassociated with that asset. Other parsing methods could additionally oralternatively be used, depending on the type of asset.

After parsing the communication into one or more blocks, based on typeof asset, the method 500 includes determining one or more discretemarketing messages 538 being communicated, by analyzing each segmentedcomponent from the parsed asset. As shown in FIG. 5, this analysis couldinclude providing the parsed components to systems 530, 532, 534, and/or536. Systems 530, 532, 534, and/or 536, described in further detailbelow, could each include one or a plurality of machine learning models,applied either in series or parallel (or both). Alternatively, thesystems 530, 532, 534, and/or 536 could include one or a plurality ofnatural language models, applied either in series or parallel (or both).As yet another alternative, the systems 530, 532, 534, and/or 536 couldinclude at least one machine learning model and at least one naturallanguage model. As still yet another alternative, the systems 530, 532,534, and/or 536 could subject the asset components to one or more rules,as described in further detail below.

Video ML/NL system 532 and Audio ML/NL system 534 are illustrated withhashed lines to indicate that video and audio may be first converted toimage and/or text, respectively (such as through transcription or videosegmenting). As such, the systems 530, 532, 534, and/or 536 need notinclude a model for each of the image, video, audio, and text types.Moreover, for some assets being analyzed, systems 530, 532, 534, and/or536 might not identify any discrete messages in the asset beinganalyzed.

The machine learning and/or natural language processing systems 530,532, 534, 536 will now be described in further detail, with reference tothe following examples.

In a first example, a machine-learning system, such as one utilizing thepipelines 300 and/or 400 illustrated in FIGS. 3 and 4 could be used toidentify, e.g., at a sentence level, when certain discrete marketingmessages are being conveyed. Training such a machine-learning system mayinclude providing a large set of training data in which particularsentences, fragments, phrases, and/or words are associated withparticular discrete marketing messages. In such an embodiment, atraining dataset large enough to achieve an acceptable accuracy in thepredictive model is recommended. Such a training dataset (e.g., the“Corpa” training dataset) will typically, but need not, includethousands of text blocks, e.g., from many companies and/or manyindustries. As another example, a crowdsourcing website, such as theAmazon Mechanical Turk crowdsourcing site (available athttps://www.mturk.com/), could be used to compile training data. In thisscenario, the method 500 may include transmitting data representing amultitude of sentences (or other word groupings) to the crowdsourcingwebsite, which, in turn, presents the data to actual humans (e.g., viatheir networked computing devices) for feedback on what marketingmessage each sentence communicates, and that feedback is provided backto the system 500. Such marketing message feedback could be received andstored to constitute training data for the predictive models set forthherein.

In addition to (or as an alternative to) the machine-learning analysisexample presented above, a rules-based analysis could be performed. Forexample, the system could maintain (i.e., store and/or access, such asvia a networked client-server relationship) a dictionary of wordsidentifying when particular discrete marketing messages, such as productfeatures or product types, are being described. Particular words orphrases might identify when a product feature (e.g., relating to acarbonated beverage) is being described in the beverage industry. Assuch, a “sparkling beverage” product feature might be identified bycomparing a parsed text block with a specialized beverage industrydictionary. An example rules-based version of systems 530, 532, 534,and/or 536 could include identifying word roots from the parsed textasset component 528.

Another example rules-based analysis of parsed communications couldinclude syntax pattern-matching. For example, the method 500 couldinclude one or more of systems 530, 532, 534, and/or 536 (a) identifyingthat a sentence begins with a verb (e.g., “contact us to learn more”) ina second-person voice, and (b) matching that particular syntax with a“call to action” discrete marketing message. Other syntax patternmatches could also be used.

Yet another example rules-based analysis of parsed communications couldinclude regular-expression pattern-matching. The method 500 couldinclude identifying regular expressions that are specific to aparticular industry. In the software industry, for example, the method500 may include recognizing synonyms for “writing,” “faster,” and“software” in marketing messages as relating to coding software morequickly. This recognition of synonyms common in the software industrycould take place regardless of the order in which the synonyms appear inthe sentence, phrase, paragraph, or other parsed text block.

The above-mentioned rules-based analysis techniques may also help toproduce training datasets. For example, a natural language processingrule could tag a parsed component as relating to a particular discretemarketing message. Tagging of parsed components could be performed byone or more humans or machine entities. In particular, in one exampleembodiment, the publicly available BART-MNLI model could be trained totag parsed components as relating to particular discrete marketingmessages. The BART-MNLI model and other “zero-shot” models mayadvantageously combine (a) context-specific understanding of the meaningof words provided through word embedding with (b) the simplicity andspeed of rules-based systems.

The above-described examples primarily relate to text-based segmentedcomponents. Segmented components of types other than text (e.g., images,video, and/or audio) may similarly be analyzed to extract discretemarketing messages. For example, one method of analyzing images or videoincludes first determining, such as via machine learning, the contentsof the images or videos (e.g., objects, concepts, emotions, people,demographics of the people, facial expressions, etc.). Second,rules-based methods, similar to those described above with respect totext-based assets, may be used to map the contents to discrete marketingmessages. Other techniques may additionally or alternatively be used forcontent-determining and mapping.

According to example embodiments, discrete marketing messages fallwithin categories such as value propositions, statements ofdifferentiation, product experiences, product features, brandarchetypes, brand promise, brand vision, brand mission, brandpositioning, brand values, brand reputation, brand archetypes, calls toaction, audience demographics, audience pastimes, audience habitats,audience values, audience personality traits, audience lifestyles, andaudience professions, among others. An example value proposition is fora beverage maker to claim that its water physically and mentallyhydrates. An example statement of differentiation is that a particularbrand of cola has a better cola taste than any other cola. An example ofa product experience is for a beverage maker to claim an experience ofhaving fun while enjoying a particular beverage, while hanging out withfriends (more about what people are doing when drinking that particularbeverage, than about the beverage itself.). Product features can simplyrefer to or describe characteristics of a particular product. Otherdiscrete marketing messages can communicate the personality (e.g.,creative or rebellious) of the brand and the customers the brand isintended to serve, as well as the archetypes (e.g. “Rebel,” “Sage,” or“Lover”) of the brand and the customers the brand is intended to serve.

FIG. 5 illustrates the one or more identified discrete marketingmessages being passed from ML/NL/Rules systems 530, 532, 534, and/or 536to block 540, in which at least one score is determined for each of theone or more discrete marketing messages determined in ML/NL/Rulessystems 530, 532, 534 and/or 536. Once a collection of discretemarketing messages is identified, scores are calculated (or otherwisedetermined) to understand the relative emphasis of each marketingmessage. While block 540 is illustrated as a single block following themultiple system blocks 530, 532, 534, and/or 536, in some examples, thefunctionality of block 540 (calculating scores associated with discretemarketing messages) may be partially or entirely incorporated into oneor more of the systems 530, 532, 534, and/or 536. Example scores includebut are not limited to: a) counts of how many times a discrete marketingmessage is communicated in text, images, videos, and within the entiremarketing asset, b) counts of how many times a discrete marketingmessage category (e.g., value proposition, brand archetype, audiencehabitat) was communicated in text, images, videos, and within the entiremarketing asset, c) the percentage of all discrete marketing messagesthat an individual discrete marketing messages or category represents(count divided by the sum of all counts). The determined scores are thenused as the independent variables within a trained predictive model forpredicting a marketing outcome, as described below with respect to block542. Moreover, scores may be used as training scores during the trainingof the trained predictive model, as described in further detail below.

The method 500 then includes inputting the determined scores into atrained predictive model to obtain a predicted marketing outcome, asshown in block 542. The scores (and possibly other data) associated withthe discrete marketing messages are the independent variables and themarketing outcome data are the dependent variables. Training of thepredictive model is described in further detail below, and generallywith reference to FIGS. 3 and 4 and accompanying description. Thepredicted marketing outcome output by the trained predictive modelrelates to a result invoked by a particular marketing or salescommunication. The marketing outcome could be a predicted engagementscore, a web page bounce rate, a clickthrough rate, an occurrence of asale, an occurrence of an engagement, or a specified conversion rate,for example. As other examples, for a website, a predicted marketingoutcome could be users selecting a “click and buy” icon for a particularproduct. On a social media communication, a predicted marketing outcomecould be to “like” or “share” a particular social media post. For abanner ad on a website, a predicted marketing outcome could be to clickthe banner ad to result in a site visit. Generally, a predictedmarketing outcome could be any consumer or business action desired to beprompted based on marketing materials. In some examples, products (e.g.,Coca-Cola) might not be purchased online; however, brand affection mightstill be predicted and gauged, such as via tracking “likes,” “follows,”or “shares” on social media.

The trained predictive model described with reference to block 542 istrained in advance of receiving the marketing asset (block 502), as partof a training process (see FIGS. 3 and 4 and accompanying description.).Training of the predictive model may involve, for example, utilizing atraining set having a multitude (perhaps millions or more) of scores(and possibly other data) associated with discrete marketing messages,along with corresponding known marketing outcome data. As anotherexample, training of the predictive model may involve using a methodhaving one or more blocks similar to blocks 502-540, which parse amultitude of marketing assets (having known marketing outcomes) intosegmented components, for which a corresponding multitude of discretemarketing messages and associated scores are determined for use intraining a predictive model. The scores (and possibly other data)associated with the discrete marketing messages are the independentvariables and the marketing outcome data are the dependent variables.Similar to as described above with respect to block 542 for using atrained predictive model to predict a marketing outcome, the trainingmarketing outcome could be an engagement score, a web page bounce rate,a clickthrough rate, an occurrence of a sale, an occurrence of anengagement, or a specified conversion rate associated with each of thediscrete marketing messages (or assets) used for training, for example.As other examples, for a website, a training marketing outcome could beusers selecting a “click and buy” icon for a particular product. On asocial media communication, a training marketing outcome could be to“like” or “share” a particular social media post. For a banner ad on awebsite, a training marketing outcome could be to click the banner ad toresult in a site visit. Other marketing outcomes could also be used fortraining.

In some examples, training a predictive model could optionally includedetermining a subset of the discrete marketing messages and/orassociated scores that each have more than a threshold correlation to anassociated marketing outcome. In other words, it is preferable but notrequired, that the predictive model training process involvesdetermining which of the identified discrete messages from systems 530,532, 534, and/or 536 are correlated with a marketing outcome. Somediscrete marketing messages might have very little or no correlationwith a marketing outcome, while other discrete marketing messages mighthave a high correlation with a marketing outcome. In some examples,determining correlation might involve combining one or more discretemarketing messages identified by systems 530, 532, 534, and/or 536. Inother examples, all discrete marketing messages identified by systems530, 532, 534, and/or 536 will be part of a determined subset, so thatthe subset does not constitute a reduced number of discrete marketingmessages.

The correlation between discrete marketing messages and marketingoutcomes can be determined based on data output by one or morepredictive models (e.g., machine learning models) being used. Many suchmodels provide correlation data, based on minimizing errors or othertechniques. One example correlation measure is Pearson's Product-Momentcoefficient (Pearson's r), which specifies a particular model'scorrelation to a data set as being between −1 and 1, where +1 is anexact positive correlation (positive slope) and −1 is an exact negativecorrelation (negative slope). Determining whether a discrete marketingmessage has more than a threshold correlation to an associated marketingoutcome may include determining that Pearson's r coefficient for aparticular model is greater than a particular value, such as an absolutevalue greater than +/−0.8, for example. Other correlation measures(e.g., least squares and others) and other threshold correlations may beused; the above are merely examples.

The method 500 uses discrete marketing messages, rather than raw data(e.g., word frequency, text vectorization (word embeddings),demographics, emotions on faces). Raw data has demonstrated to be toofragmented and cumbersome to be predictive. This is because raw data byitself has been found to produce too many predictive variables, requiretoo much training data, and reduce the ability to infer causality from apredictive model. But when raw data is instead translated into discretemarketing messages, which might, for example, turn 100 independentvariables into a single “discrete marketing message” independentvariable, better prediction results have been realized. This correspondsto the feature-engineering layer of machine learning (see, e.g., featurevector(s) 322 in FIG. 3). Such features can be used in standard MLmodels, like Ridge regression and Lasso regression, for example.Regression models tend to work best in marketing and sales contextsbecause the predicted marketing outcome can take the form of acontinuous number (e.g., from 0 to X) that the method 500 is trying topredict. This is in contrast to classification models, which simplyattempt to provide yes/no predictions, such as whether an image portraysa cat. In some example embodiments, raw data can be used to helpsupplement a prediction supplied by the machine learning model thatutilizes the predictive variable relying on the discrete marketingmessage.

The discrete marketing messages that are correlated with marketingoutcomes are highly contextual. The components of the context couldinclude, but are not limited to, the industry, the channel (email, web,social, etc.), the company characteristics, the marketing asset'sobjective (to sell, to inform, etc.), and the audience attributes.Therefore, in some embodiments, building an accurate predictive modelincludes training the model using marketing assets having a similarcontext. Similarly, trained models could be categorized and selectedbased on context, in order to improve prediction accuracy.

Returning to FIG. 5, after inputting the determined discrete messagesand the determined associated scores into the trained predictive model,the predicted marketing outcome can be output 544, such as by displayingin a user interface (see, e.g., the screen shots of FIGS. 6A-6R, 7, and8, illustrating output of a marketing outcome prediction) and/orprinting in a printed output. Additionally or alternatively, thepredicted marketing outcome can be stored 546 in a storage location(e.g., a snapshot of results published to a storage box location, suchas those offered by box, google drive, or dropbox), provided as an inputto a marketing automation platform, such as one offered by Hubspot,Marketo, Mailchimp, or Salesforce, providing as an input to a digitalasset management (DAM) system, such as one offered by bynder orBrandfolder, providing as a user experience (UX) input to a creativeprocess system, or providing in a report, for example. The output couldbe presented in a web-based Software-as-a-Service (SaaS) user interface,in one example. Such a SaaS user interface could serve as a web-baseddashboard into which the predicted marketing outcome is pushed. Forexample, the output might include a report of predicted social mediaoutcomes, a predicted number of consumer comments, a predicted number ofconsumer evaluations (e.g., “likes” or “loves”), and/or a predictednumber of consumer “shares.” In some embodiments, the output may includepredictions along with recommendations that tell how to improveperformance.

FIG. 6A-6R are pictorial diagrams illustrating example screenshotdisplays showing example correlated discrete marketing messages(predictive/independent variables) and associated scores for each ofthose correlated discrete marketing messages. Such scores (e.g.,relative emphasis or density within a particular asset) may be used totrain a predictive model, as was described above. These same scores mayalso be used by the trained predictive models to predict a marketingoutcome, as was described above with respect to block 542 in FIG. 5.Each of FIGS. 6A-6R is briefly described below for the purpose ofproviding illustrative examples only; other industries, customers,products, etc., may have different types of discrete marketing messagesand predicted marketing outcomes, according to example implementations.FIGS. 6A-6M show correlated discrete marketing messages for the assetbeing analyzed (or for a user having a suite of assets), while FIGS.6N-6R show a comparative analysis between correlated discrete marketingmessages of the user versus those of a competitor.

FIG. 6A illustrates a graphical display 602 of brand attributes 604listed along a y-axis and relative emphasis 606 (in percentage) listedalong an x-axis. As shown, the brand attributes 604 include valueproposition, differentiation, reputation, brand promise, values, productfeatures, product experience, and personas. Emphasis for each of thesebrand attributes ranges from 0% to 17%, with product features and valueproposition being highest. A legend 608 provides information relating tobrand attribute emphasis for a particular marketing asset beinganalyzed.

FIG. 6B illustrates a tabular display 610 of keyword density 612, witheach keyword tab also including indicator of density. As shown, thekeywords used most frequently in the asset being analyzed include “your”(1076), you (739), mailchimp (385), marketing (229), we (217), audience(182), and so on. A legend 615 provides information relating to keyworddensity, and explains a feature of highlighting one or more tabs as redor green (not shown in FIG. 6B) to signify keywords being brand matchesor off-brand, respectively.

FIG. 6C illustrates a tabular display 616 of image objects and contents618, with each image tab also including indicator of density. As shown,the images used most frequently in the asset being analyzed include noperson (939), dark (663), nature (611), moon (587), bright (529),monochrome (475), eclipse (465), and so on. A legend 620 providesinformation relating to image contents, and explains a feature ofhighlighting one or more tabs as red or green (not shown in FIG. 6B) tosignify images being brand matches or off-brand, respectively.

FIG. 6D illustrates a graphical display 622 of product features 624listed along a y-axis and relative emphasis (in percentage) listed alongan x-axis. As shown, the product features 624 include helpful content,email marketing, analytics, market segmentation, marketing automationplatform, templates, landing page builder, advertising management,postcard marketing, and campaign management. Emphasis for each of theseproduct features ranges from 6% to 17%, with helpful content and emailmarketing being highest. A legend 626 provides information relating toproduct feature emphasis for a particular marketing asset beinganalyzed.

FIG. 6E illustrates a graphical display 628 of product experience 630listed along a y-axis and relative emphasis (in percentage) listed alongan x-axis. As shown, the product experiences 630 include easy-to-use,flexible pricing, and affordable pricing. Emphasis for each of theseproduct experiences ranges from 29% to 41%, with easy-to-use beinghighest. A legend 632 provides information relating to productexperience emphasis for a particular marketing asset being analyzed.

FIG. 6F illustrates a graphical display 634 of differentiations 636listed along a y-axis and relative emphasis (in percentage) listed alongan x-axis. As shown, the differentiations 636 include free, top ranked,award-winning, and better support. Emphasis for each of thesedifferentiations ranges from 3% to 79%, with free being highest. Alegend 638 provides information relating to differentiation emphasis fora particular marketing asset being analyzed.

FIG. 6G illustrates a graphical display 640 of value propositions 642listed along a y-axis and relative emphasis (in percentage) listed alongan x-axis. As shown, the value propositions 642 include increaserevenue, customer acquisition, build relationships, increase engagement,increase traffic, ROI, hit your goals, build a brand, increase loyalty,and increase conversion. Emphasis for each of these value propositionsranges from 2% to 41%, with increase revenue and customer acquisitionbeing highest. A legend 644 provides information relating to valueproposition emphasis for a particular marketing asset being analyzed.

FIG. 6H illustrates a graphical display 646 of archetypes 648 listedalong a y-axis and relative emphasis (in percentage) listed along anx-axis. As shown, the archetypes 648 include creator, sage, lover,innocent, explorer, jester, everyman, ruler, hero, and caregiver.Emphasis for each of these archetypes ranges from 0% to 58%, withcreator being highest. A legend 650 provides information relating toarchetype emphasis for a particular marketing asset being analyzed.

FIG. 6I illustrates a graphical display 652 of reputations 654 listedalong a y-axis and relative emphasis (in percentage) listed along anx-axis. As shown, the reputations 654 include simple, innovative, andtrusted, ranging from 2% to 52%, with simple and innovative beinghighest. A legend 656 provides information relating to reputationemphasis for a particular marketing asset being analyzed.

FIG. 6J illustrates a graphical display 658 of habitats 658 listed alonga y-axis and relative emphasis (in percentage) listed along an x-axis.As shown, the habitats 658 include a single “urban” habitat having anemphasis of 100%. A legend 662 provides information relating to habitatemphasis for a particular marketing asset being analyzed.

FIG. 6K illustrates a graphical display 664 of pastimes 666 listed alonga y-axis and relative emphasis (in percentage) listed along an x-axis.As shown, the pastimes 666 include creating art, enjoying nature,travel, photography, social media, sports, shopping, cooking, writing,and running/jogging. Emphasis for each of these pastimes ranges from 1%to 55%, with creating art and enjoying nature being highest. A legend668 provides information relating to pastimes emphasis for a particularmarketing asset being analyzed.

FIG. 6L illustrates a graphical display 670 of personalities 672 listedalong a y-axis and relative emphasis (in percentage) listed along anx-axis. As shown, the personalities 672 include creative, fun-loving,health-conscious, fashionable, refined, cool, hardworking, energetic,ambitious, and unique. Emphasis for each of these personalities rangesfrom 1% to 58%, with creative being highest. A legend 674 providesinformation relating to personalities emphasis for a particularmarketing asset being analyzed.

FIG. 6M illustrates a graphical display 676 of professions 678 listedalong a y-axis and relative emphasis (in percentage) listed along anx-axis. As shown, the professions 678 include sales and marketing,education, information worker, construction/craftsman, informationtechnology, entrepreneur, writer, arts, agriculture, and journalism.Emphasis for each of these professions ranges from 1% to 58%, withcreative being highest. A legend 680 provides information relating toprofessions emphasis for a particular marketing asset being analyzed.

FIG. 6N illustrates a graphical display 682 of brand attributes 684listed along a y-axis and relative emphasis (in percentage) listed alongan x-axis. As shown, brand attributes are listed for both a particularasset or user and for a competitor, such as a competitor pre-specifiedby the user for comparison. The brand attributes 684 shown include valueproposition, differentiation, reputation, brand promise, values, productfeatures, product experience, and personas. The highest emphasis forboth the user and competitor is on product features, with the competitorplacing a slightly higher emphasis (23%) compared to the user (17%). Alegend 686 provides information relating to brand attribute emphasiscompared to competitors.

FIG. 6O illustrates a graphical display 688 of differentiations 689listed along a y-axis and relative emphasis (in percentage) listed alongan x-axis. As shown, differentiations 689 are listed for both aparticular asset or user and for a competitor, such as a competitorpre-specified by the user for comparison. The differentiations 689 showninclude free, top ranked, award-winning, and better support. The highestemphasis for both the user and competitor is free, with the competitorplacing a higher emphasis (90%) compared to the user (79%), while theuser places a higher emphasis (13%) on top ranked compared to thecompetitor (10%). A legend 690 provides information relating todifferentiations emphasis compared to competitors.

FIG. 6P illustrates a graphical display 688 of product features 692listed along a y-axis and relative emphasis (in percentage) listed alongan x-axis. As shown, product features 692 are listed for both aparticular asset or user and for a competitor, such as a competitorpre-specified by the user for comparison. The product features 692 showninclude helpful content, email marketing, analytics, marketsegmentation, marketing automation platform, templates, landing pagebuilder, advertising management, postcard marketing, campaignmanagement, CRM, automation integrations, meeting scheduler, socialmedia marketing, online chat, and user management. The highest emphasisfor the competitor is placed on CRM, templates, and email marketing,while the highest emphasis for the user is placed on helpful content andemail marketing. A legend 693 provides information relating to productfeature emphasis compared to competitors.

FIG. 6Q illustrates a graphical display 694 of personalities 695 listedalong a y-axis and relative emphasis (in percentage) listed along anx-axis. As shown, personalities 695 are listed for both a particularasset or user and for a competitor, such as a competitor pre-specifiedby the user for comparison. The personalities 695 shown includecreative, fun-loving, health-conscious, fashionable, refined, cool,hardworking, energetic, ambitious, unique, trendy, and progressive. Thehighest emphasis for both the user and the competitor is placed oncreative (58% and 36%). A legend 696 provides information relating topersonality emphasis compared to competitors.

FIG. 6R illustrates a graphical display 697 of pastimes 698 listed alonga y-axis and relative emphasis (in percentage) listed along an x-axis.As shown, pastimes 698 are listed for both a particular asset or userand for a competitor, such as a competitor pre-specified by the user forcomparison. The pastimes 698 shown include creating art, enjoyingnature, travel, photography, social media, sports, shopping, cooking,writing, running/jogging, driving, and arts and crafts. The highestemphasis for both the user and the competitor is placed on creating art(55% and 50%), followed by enjoying nature (31% and 21%). A legend 690provides information relating to pastimes emphasis compared tocompetitors.

FIG. 7 is a pictorial screenshot diagram illustrating example output 700of an example marketing outcome prediction. The output 700 includes theasset/communication being analyzed—in this case, an image 702 of amountain bike and corresponding text 704 relating to the image 702. Theoutput also includes a predicted marketing outcome 706—in this case apredicted engagement score of 46%. Finally, the illustrated exampleincludes tips 708 and 710 on how to increase visual and textualengagement.

FIG. 8 is a pictorial screenshot diagram illustrating an example userinterface 800 in the form of a dashboard report providing examplemarketing-outcome prediction information. The user interface 800 canprovide outputs including a brand identification 802, channel 804, agraphical plot 806 of messaging emphasis for the specific brand andchannel, a graphical plot 808 of competitor messaging emphasis, a chart810 graphically illustrating a best practices and standards score (81%)in this example, and a plot 812 showing a best practices and standardsadherence trend over time. In the plot 812, in three out of four timeperiods, the best practices and standards adherence score was 81%. Inone of the four time periods, the score was 82%, a difference of 1%.That difference is illustrated in chart 810 as segment 814, signifying aportion decreased (if in red) or increased (if in green) from a previoustime period's score. The metrics and analytics illustrated in FIG. 8 aremerely examples, and many other or additional metrics, analytics, andother dashboards may be included or substituted. For example, theinformation illustrated in FIGS. 6A-6R may be included in the userinterface 800. The user interface 800 may also receive user inputs, suchas to select or modify which information is displayed.

The description of the different advantageous arrangements has beenpresented for purposes of illustration and description, and is notintended to be exhaustive or limited to the embodiments in the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art. Further, different advantageousembodiments may provide different advantages as compared to otheradvantageous embodiments. The embodiment or embodiments selected arechosen and described in order to best explain the principles of theembodiments, the practical application, and to enable others of ordinaryskill in the art to understand the disclosure for various embodimentswith various modifications as are suited to the particular usecontemplated.

What is claimed:
 1. A method for predicting an outcome for a marketingasset, comprising: determining by a computing device, for a marketingasset received by the computing device, at least one asset type of themarketing asset; parsing, by the computing device, the marketing assetinto a plurality of segmented components, based on the determined atleast one asset type; determining, for at least one of the plurality ofsegmented components, at least one discrete marketing message byapplying at least one of a predictive model or rules to the at least onesegmented component, wherein the at least one predictive model or rulesare stored in a memory associated with the computing device;determining, for each of the at least one discrete marketing message, atleast one associated score; and inputting the determined at least oneassociated score to a trained predictive model to obtain a predictedmarketing outcome, wherein the trained predictive model was trainedusing training scores associated with a multitude of training discretemarketing messages as independent variables and corresponding trainingmarketing outcome data as dependent variables, and wherein the trainedpredictive model is stored in the memory associated with the computingdevice.
 2. The method of claim 1, further comprising providing via agraphical interface, an output related to the predicted marketingoutcome.
 3. The method of claim 1, further comprising storing, in thememory associated with the computing device, data relating to thepredicted marketing outcome.
 4. The method of claim 1, wherein the atleast one associated score is based on at least one of the following:(a) how many times a particular discrete marketing message iscommunicated in the marketing asset or (b) where, in the marketingasset, the particular discrete marketing message is located.
 5. Themethod of claim 1, wherein the plurality of segmented componentscomprises at least one of the following: text, image, audio, or video.6. The method of claim 5, wherein, if one of the plurality of segmentedcomponents comprises audio, the method further comprises transcribingthe audio into text.
 7. The method of claim 5, wherein, if one of theplurality of segmented components comprises video, the method furthercomprises: splitting the video component into a plurality of imagecomponents and an audio component; and transcribing the audio component.8. The method of claim 1, wherein parsing the marketing asset into theplurality of segmented components comprises, if the marketing assetincludes a web page: crawling the web page; identifying html code in theweb page; storing the identified html code in the memory associated withthe computing device; and parsing the stored identified html code byidentifying at least one html tag, wherein the at least one html tagidentifies at least one of an image, headline text, sub-headline text,paragraph text, video, or other component.
 9. The method of claim 1,wherein parsing the marketing asset into the plurality of segmentedcomponents comprises, if the marketing asset includes a social mediapost, using an Application Program Interface (API) for a social mediaapp associated with the social media post to parse the social media postinto at least one of image, text, audio, or video.
 10. The method ofclaim 1, wherein parsing the marketing asset into the plurality ofsegmented components comprises, if the marketing asset includes aportable document format (pdf) file, using a pdf parser to parse thesocial media post into at least one of image, text, audio, or video. 11.The method of claim 1, wherein parsing the marketing asset into theplurality of segmented components comprises, if the marketing assetincludes a web-based word-processing application file, using anApplication Program Interface (API) to parse the web-basedword-processing application file into at least one of image, text,audio, or video.
 12. The method of claim 1, wherein parsing themarketing asset into the plurality of segmented components comprises, ifthe marketing asset includes a plain-text email, parsing a text block inthe plain-text email.
 13. The method of claim 1, wherein parsing themarketing asset into the plurality of segmented components comprises, ifthe marketing asset includes an html email, identifying at least onehtml tag in the html email, wherein the at least one html tag identifiesat least one of an image, headline text, sub-headline text, paragraphtext, video, or other component.
 14. The method of claim 1, whereinparsing the marketing asset into the plurality of segmented componentscomprises, if the marketing asset includes a video: segmenting the videointo temporal blocks; parsing each temporal block into audio and images;and transcribing the audio for each temporal block into text, whereinthe transcribing includes using an audio transcription service.
 15. Themethod of claim 1, wherein parsing the marketing asset into theplurality of segmented components comprises, if the marketing assetincludes an audio sales call: segmenting the audio sales call intotemporal blocks each having audio; transcribing the audio for eachtemporal block into text, wherein the transcribing includes using anaudio transcription service.
 16. The method of claim 1, whereindetermining the at least one discrete marketing message comprisesapplying natural-language rules to the at least one segmented componentof the marketing asset to determine at least one of a word root orsyntax pattern, and wherein the natural-language rules are stored in thememory associated with the computing device.
 17. The method of claim 1,wherein the at least one discrete marketing message is from a discretemarketing message category selected from the following: valuepropositions, statements of differentiation, product experiences,product features, brand archetypes, brand promises, brand visions, brandmissions, brand positionings, brand purposes, brand values, brandreputations, brand archetypes, calls to action, audience demographics,audience pastimes, audience habitats, audience values, audiencepersonality traits, audience lifestyles, or audience professions. 18.The method of claim 1, wherein determining the at least one discretemarketing message comprises accessing at least one industry-specificdictionary for at least one segmented component of the marketing asset,and wherein the at least one industry-specific dictionary is stored inthe memory associated with the computing device.
 19. The method of claim1, wherein the predicted marketing outcome is selected from the groupconsisting of a predicted engagement score, a web page bounce rate, aclickthrough rate, an occurrence of a sale, an occurrence of anengagement, or a specified conversion rate.
 20. The method of claim 1,further comprising: determining a subset of discrete marketing messages,wherein the subset comprises only those determined discrete marketingmessages having more than a threshold correlation to an associatedmarketing outcome; and providing a recommendation associated with thepredicted marketing outcome, wherein the recommendation relates to thedetermined subset of discrete marketing messages.
 21. A system forpredicting an outcome for a marketing asset, comprising: at least onecomputing device to perform tasks comprising: determining, for amarketing asset, at least one asset type of the marketing asset; parsingthe marketing asset into a plurality of segmented components, based onthe determined at least one asset type; determining, for at least one ofthe plurality of segmented components, at least one discrete marketingmessage conveyed by the marketing asset, wherein the determiningincludes applying at least one of a predictive model or rules to the atleast one segmented component; determining, for each of the at least onediscrete marketing message, at least one associated score; and inputtingthe determined at least one associated score to a trained predictivemodel to obtain a predicted marketing outcome, wherein the trainedpredictive model was trained using training scores associated with amultitude of training discrete marketing messages as independentvariables and corresponding training marketing outcome data as dependentvariables.
 22. The system of claim 21, wherein the tasks performed bythe at least one computing device further comprise providing via agraphical interface, an output related to the predicted marketingoutcome.
 23. The system of claim 21, wherein the at least one associatedscore is based on at least one of the following: (a) how many times aparticular discrete marketing message is communicated in the marketingasset or (b) where, in the marketing asset, the particular discretemarketing message is located.
 24. The system of claim 21, wherein theplurality of segmented components comprises at least one of thefollowing: text, image, audio, or video.
 25. An article of manufactureincluding a non-transitory computer-readable medium, having storedthereon program instructions that, upon execution by one or moreprocessors, cause the one or more processors to perform taskscomprising: determining, for a marketing asset, at least one asset typeof the marketing asset; parsing the marketing asset into a plurality ofsegmented components, based on the determined at least one asset type;determining, for at least one of the plurality of segmented components,at least one discrete marketing message conveyed by the marketing asset,wherein the determining includes applying at least one of a predictivemodel or rules to the at least one segmented component; determining, foreach of the at least one discrete marketing message, at least oneassociated score; and inputting the determined at least one score to atrained predictive model to obtain a predicted marketing outcome,wherein the trained predictive model was trained using training scoresassociated with a multitude of training discrete marketing messages asindependent variables and corresponding training marketing outcome dataas dependent variables.
 26. The article of manufacture of claim 25,wherein the tasks performed by the one or more processors furthercomprise providing via a graphical interface, an output related to thepredicted marketing outcome.
 27. The article of manufacture of claim 25,wherein the at least one associated score is based on at least one ofthe following: (a) how many times a particular discrete marketingmessage is communicated in the marketing asset or (b) where, in themarketing asset, the particular discrete marketing message is located.28. The article of manufacture of claim 25, wherein the plurality ofsegmented components comprises at least one of the following: text,image, audio, or video.